{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir('D:/Practical Time Series/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_excel('datasets/Monthly_CO2_Concentrations.xlsx',\n",
    "                     converters={'Year': np.int32, 'Month': np.int32})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>333.13</td>\n",
       "      <td>1974</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>332.09</td>\n",
       "      <td>1974</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>331.10</td>\n",
       "      <td>1974</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>329.14</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>327.36</td>\n",
       "      <td>1974</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CO2  Year Month\n",
       "0  333.13  1974     5\n",
       "1  332.09  1974     6\n",
       "2  331.10  1974     7\n",
       "3  329.14  1974     8\n",
       "4  327.36  1974     9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CO2</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974-05</th>\n",
       "      <td>333.13</td>\n",
       "      <td>1974</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-06</th>\n",
       "      <td>332.09</td>\n",
       "      <td>1974</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-07</th>\n",
       "      <td>331.10</td>\n",
       "      <td>1974</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-08</th>\n",
       "      <td>329.14</td>\n",
       "      <td>1974</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-09</th>\n",
       "      <td>327.36</td>\n",
       "      <td>1974</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            CO2  Year Month\n",
       "1974-05  333.13  1974     5\n",
       "1974-06  332.09  1974     6\n",
       "1974-07  331.10  1974     7\n",
       "1974-08  329.14  1974     8\n",
       "1974-09  327.36  1974     9"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create row index of the DataFrame using the Year and Month columns\n",
    "data.index = data['Year'].astype(str) + '-' + data['Month'].astype(str).map(lambda x: '0'+x if len(x) == 1 else x)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data.loc[(~pd.isnull(data['CO2']))&\\\n",
    "                (~pd.isnull(data['Year']))&\\\n",
    "                (~pd.isnull(data['Month']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.sort_values(['Year', 'Month'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zxd9z8a1vafz+llvyn1eobINP9Uwq/JmF2tauLb7NMF26aAfxqlWlG7xVTRQU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\n8ensRcQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1410bf710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "data['CO2'].plot(color='b')\n",
    "plt.title('Monthly CO2 concentrations')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('CO2 concentratition')\n",
    "plt.xticks(rotation=30)\n",
    "plt.savefig('plots/ch1/B07887_01_05.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KPvnEo0OyxQW7abIZdDwPKAeWqOrBwD7A6ixetxEYq6p7Y8mijhSRfQFEZABw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\nE1ywHcdxYoILtuM4Tkz4/yzo5VfacOGSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f140efc9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "data['CO2'].loc[(data['Year']==1980) | (data['Year']==1981)].plot(color='b')\n",
    "plt.title('Monthly CO2 concentrations')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('CO2 concentratition')\n",
    "plt.xticks(rotation=30)\n",
    "plt.savefig('plots/ch1/B07887_01_06.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend_model = LinearRegression(normalize=True, fit_intercept=True)\n",
    "trend_model.fit(np.arange(data.shape[0]).reshape((-1,1)), data['CO2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trend model coefficient=0.1209930124223602 and intercept=329.45310559006214\n"
     ]
    }
   ],
   "source": [
    "print('Trend model coefficient={} and intercept={}'.format(trend_model.coef_[0],\n",
    "                                                           trend_model.intercept_)\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "residuals = np.array(data['CO2']) - trend_model.predict(np.arange(data.shape[0]).reshape((-1,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gtJtuss/r1unHhuqaO6F7yCZPvgDGmC3GmJuyn/cD3wPOrvs7bZIftGsg/KTs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\nIAiCHiYq+SAIgh7mv9/a6iTykXwXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f143288e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "pd.Series(data=residuals, index=data.index).plot(color='b')\n",
    "plt.title('Residuals of trend model for CO2 concentrations')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('CO2 concentratition')\n",
    "plt.xticks(rotation=30)\n",
    "plt.savefig('plots/ch1/B07887_01_07.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['Residuals'] = residuals\n",
    "month_quarter_map = {1: 'Q1', 2: 'Q1', 3: 'Q1',\n",
    "                     4: 'Q2', 5: 'Q2', 6: 'Q2',\n",
    "                     7: 'Q3', 8: 'Q3', 9: 'Q3',\n",
    "                     10: 'Q4', 11: 'Q4', 12: 'Q4'\n",
    "                    }\n",
    "data['Quarter'] = data['Month'].map(lambda m: month_quarter_map.get(m))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "seasonal_sub_series_data = data.groupby(by=['Year', 'Quarter'])['Residuals'].aggregate([np.mean, np.std])\n",
    "seasonal_sub_series_data.columns = ['Quarterly Mean', 'Quarterly Standard Deviation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Quarterly Mean</th>\n",
       "      <th>Quarterly Standard Deviation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1974-Q2</th>\n",
       "      <td>1974</td>\n",
       "      <td>Q2</td>\n",
       "      <td>3.096398</td>\n",
       "      <td>0.820946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-Q3</th>\n",
       "      <td>1974</td>\n",
       "      <td>Q3</td>\n",
       "      <td>-0.616085</td>\n",
       "      <td>1.991671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-Q4</th>\n",
       "      <td>1974</td>\n",
       "      <td>Q4</td>\n",
       "      <td>-1.822397</td>\n",
       "      <td>1.014952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-Q1</th>\n",
       "      <td>1975</td>\n",
       "      <td>Q1</td>\n",
       "      <td>0.754624</td>\n",
       "      <td>0.511890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-Q2</th>\n",
       "      <td>1975</td>\n",
       "      <td>Q2</td>\n",
       "      <td>2.604978</td>\n",
       "      <td>0.355093</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Year Quarter  Quarterly Mean  Quarterly Standard Deviation\n",
       "1974-Q2  1974      Q2        3.096398                      0.820946\n",
       "1974-Q3  1974      Q3       -0.616085                      1.991671\n",
       "1974-Q4  1974      Q4       -1.822397                      1.014952\n",
       "1975-Q1  1975      Q1        0.754624                      0.511890\n",
       "1975-Q2  1975      Q2        2.604978                      0.355093"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create row indices of seasonal_sub_series_data using Year & Quarter\n",
    "seasonal_sub_series_data.reset_index(inplace=True)\n",
    "seasonal_sub_series_data.index = seasonal_sub_series_data['Year'].astype(str) + '-' + seasonal_sub_series_data['Quarter']\n",
    "seasonal_sub_series_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tvmbNbMVoraBjeNC9Yte95Q4h6DrHS1ElOw9Lgo7RSbhmzezRrD4uxFO5Fgq6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\nQkJDkQg6ISEhoaH4vwUH52VAjqhUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f14341ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "seasonal_sub_series_data['Quarterly Mean'].plot(color='b')\n",
    "plt.title('Quarterly Mean of Residuals')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('CO2 concentratition')\n",
    "plt.xticks(rotation=30)\n",
    "plt.savefig('plots/ch1/B07887_01_08.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vK+q0cejC+shS1FCfqJOCiTbw58Knjou0oKhrwB6oOFHbwKLLrA87EDqpqF16\n1ODW5+2G9QHuFfXoqN+sDxE3M/y6YX0MD5tj4MujBn+KGtz41HEyXb9+AU94EZEPi8g9IvLjlM9P\nFpE9IvKD6PG2Mu3HVyC3GBgwBOXDox4dNQrJt6J2ZX3EB4JvRe3K+kibmQjusm3iitqnRw1uUguz\niHr//np+bFYKXd0Zob6JOi09D9wo6naidrVm6vS0mQzUS9bHvwPPzPnOt1T1EdHjr8o03r5ogIXL\nwkxxwuvrgzVr/CtqH9ZHJxS1K+tjcHBurV5XaW4wd7/4tj7Ar6Jetsyc9HWqImal0NW9yPi2PtKC\nieCOqOPBxJkZtwtPe1HUItIvIltEZLt95P2Pql4GOCxlMhftK5BbuCTq9jQaV4WZkuo3WPiwPjrh\nUbtS1D4mX1j4sj66QdQu4jF5irqXrY8kwrNxJB/WB7jxqZMuMEWQS9Qi8jrgbuBrwBejx0Ul+5eG\nx4vIj0TkSyKyK6MPZ4nI1SJy9e5ob3VKUcd3qKvCTGknH7Ruyevc0iYpaldEnZX1UTctyirqOJps\nfbgoKJVH1HUyP7LItO5FpoiirjPOkwjPTkzzEUwEN0TtU1G/AThSVXep6rHR4+FlO5iA7wHbo7Y+\nCHwh7Yuqep6qnqiqJ66P9loaUbtc4Lad8Fwq6iylMT1dbw3CJEXt2/qYmamf69wJRR0n6snJauvX\ntSNr4oivCS/2TrLOWM+yPjqhqGdmqgsIW8+m/cLuanaiL0Xtk6hvAxyvMAeq+pCq7ov+vhgYFJF1\nRf+/0x41uKugl6eoof5tYaetD6j/G0ltDw+bk9GH9QFuVHVWcSOfwUTwa324CCZmKWqoPs7j9Wzi\n6HWi9mZ9ADcBl4jIn4vIm+yjbAfbISKbRMxuFpFHR30prFeziNoVKbUTnq2gV/cWvwhR1yGQJOtj\nfNzNggppWR/gh6hF3K4/GFfU4Iao02IOVlHXzczwRdSdCCamjXMb+Lvrrmrtp9XLcDWNvNcU9UCB\n79waPYaiRyGIyKeAk4F1InI78P+AQQBVPRd4HvAqEZkGxoAzVIsP6aT0PPDrUa9bZwbgvn0tFVkF\nPolaNdn6AEOka9ZUa9ciS1G7yOv1EdgCc5s9NTV3ZiK4uwCkkd3MzPziYWXbTpuZCG486qx9rlp+\nxex422nj3NZ5vvVWeMxjyrefVi/DhaKempqbQjc0ZI5lNxV1LlGr6jkAIrI8el3odFTVF+R8/iHg\nQ0XaSkIUcUL4AAAgAElEQVS3golg7A9fRF33lrB9iSKY6/PWJeqkYKJP6wPcWQgwNy8e3CnqNJ8X\nzMWgClHPzprj6VNR9/e3aovHsXKl+e2xsfnnWNG2Id36sER9yy3l24b0adgbN5qxUmWadrxtmKt6\nXU168eZRi8gxIvJ94DrgOhG5JitDo1PwnZ5nlWlS+k/dgKJPRZ10xXa9XFYnrQ9wo6jbidq19ZGm\nqKG6as9Spa486jQirZu/nqeoR0fNuLz11mrtp83uczGNPIlMXU0j9xlMPA94k6oerKoHA28G/qXc\nz7iHJer2DV6+3JDV9HS99icnDVmnKeo66DRRu1zg1qf1kbQiNrgh6vYFhV1bH2l3AnV+I4vsXFkf\naeOwbraN7Xv73ZeFiFnhuypRZylqqKd+fRK1z2DiMlX9pn2hqpcAFR03dzhwwAyy9ts2VwvcJu3Q\nTirqqtZHlqJ2kTnR6awP6H1FnZZC51OVukrPyyPqqhcZmxOfNKnLYvv2ekSdpEpdzE5sqqK+SUT+\nUkR2RI+/wGSCdBXtJU4tXN2GJxFeJxT1wIDZhrqKur3WByxu66P9eI6MmH3dVOtjaMg8etn6SBvj\nFtu3V/eos4KJ4Iao4+2vX2/O+7oZXz4V9UuB9cDno8f66L2uIo+o696GJxHeqlVGIfhU1PZ3fFgf\nLhR1kj3RiayPuhZFu/Uh4q7eRzeIGlqFmaqiiKKuOmbSjmUcBx8M999fbdxkpedBPaJOCyZOTdUf\nL97S81T1AeD1FfrkFb6JOumq2t9vsjJ8KmqoV5jJZzBxZsY82n1HOynFp6KemCim0tLQbn2Au2nk\nRbI+qiAvF3nZMn+Kum7fiypqgNtug507y7WfpqhHRsx49+FRg7E/rG3mqu0iSCVqEXmfqr5RRC4E\n5gl+VX1OuZ9yi/YVyC1cK+r2weBiGnkRRe3Do65LpEkrsMR/wydRg2m/KlEn7RdXhZnSjueKFUa5\n+1TUvjxqF9ZHnqKO51KXJeqs9Lu6udRp6zGCIeojjqje9vi4EXwDRWawxJD19fOj53dV7ZRPpE0i\n6ARR+1bUq1YZlVEFSf0eGDCDrq71kbcOngvrIylLIE4aNqBbFu3WB7i1PpLy6vv6zPu9an1kjUO7\nPXXuBooq6io+dVbxfR9E7Wp2YpVFAyDDo1bVa6I/H6Gql8YfwCOqddMduuFRQ2saeR3kEfX69dUH\nWtoFxkUFvSyi7oSidlEkyIf1kadM62Z9pClTn9bH0JDZVz6DiVu2GHVZJfMjS1Fv2OAnmAj1J72k\neet5KBJM/MOE915c/qfcIo2oXQW2kg4WdEZRH3II3Hlnqw9lkNZvFxX0mkzUSYrat/Vhf6OJ1gfU\nu8gUCSYODMDWrdWJOktR1yHUtGAiuFHUVWZMZnnULwB+HzhERC6IfbQCjwsCFEU30vNgbmGmKjUQ\noBhRA/zyl9W8O0gmalcedZI94aIYVjcUtc+sD6iXsdIJ6yOrpEDdvheJJ1TJpZ6ZMWMly6O+7z4z\n6a2sFwzJ1seSJWZ/d8v6yNqMK4A7gXXAu2Pv7wV+VP6n3CKNqF1MBIBsj3p8vHqhHVuLI2sQH3qo\neb75ZndE7SIXOU9RV62EFm/ft6JuJ+p9+6qf0BZ5irqqussj6rrWR56irjNmiihqMER9xRXl2rb7\nJUtRqxpS3by5XNuQnpnhYtJL1RokWR71Lap6iao+rs2j/p6q1pygXR9pRD04aAafT48aqvvUeScf\ntIj6pgrTinwq6m5bH3XUb5r1UbddaK71kZeZUddfL6KoDz4Ybr/dqOSiSLP3LGyGRtULZFr7Loja\neTBRRC6PnveKyEOxx14RcbReSHWkpeeBmwyELI8aqvvURYh6wwazbS6J2ncwsa71MTNj7jQ6bX1A\nfZ86b4afr8JGdpxXnS2XR6adsj6mp8vdjeXN7qs7O3FszBzP9unvrhS166yPJ0bPK1R1ZeyxQlVX\nVu9qfdhln9KsBxdEneVRg19FLWJ86ptvLt9+lqL2bX3U2edZOdp2urcP6wOaq6iXLTMXt6pLoPWK\n9QHlUvTyJo3UPUfTyNSVonZqfViIyPlF3usk7IHyqaizPGrwq6jBEHVVRT04OL9YlQtFnTfhZXKy\nReZlkXURsKu81CXqvr65XrSLmtRZNaPtb9hZlWWRNzOxbipqr1gfUC6gmKeo61paWSVUd++uV+/D\nufURw5za0yIyAJxQ/qfcIW3RAAsXfun4uCGI9oHcKaI+9FCjqMsOirRgxYoVZpDUKf9qiSMt6wOq\n7/e8QvMuFltt3y8urI88Mq3jrxexPqA6URdV1FWIqaii3rbNPFch6jTC80XU69ebY1JHBDoPJkZr\nJO4FHh73p4G7gf+u3FMHyCNqVx510uKZq1eb93xaH2AU9d695X8ni6ih3gUsz/qAeqSR1ja4UdTt\n+9yF9VGkQH7V38ib8GKJumqKXhFFPTtbrf2iinrlSnMcyhB1XjBx6VJzR+mDqKF+HRHXHvXfquoK\n4O/b/Om1qvrn1btaH50g6jTCq1uYqYyihvI+dVq/XQTkihB1ryrqpP3iwvrwTdRpS2VBvVTUPMsG\n6t8NFFHUUL7caZ6iFqkXG0jzkV1MevFmfajqn4vIahF5tIj8hn1U6aQrdJOood408rJEXdan7pai\nbqL1sXKlOaldELWPus55qrSO9ZG3v6Fe34vU+rAou9JLnqKGekSdFUyEekTt3PqwEJGXAZcBXwHO\niZ7PLv9T7pC2ArmFK6JOu/LVmUZelKh37DDProjaxXJcecFEqG99pC3dVLcmdZL10ddXv13firoI\nUVe1JsCPolYtV5K27OzEIsX36ypqH0St6jeY+AbgUcAtqvqbwPGAgwoJ1ZG2sK2FS486CZ1Q1MuX\nmyizK+vDxXJcWWTa69ZHkqKG+vU+fAcTs8ZJHesjr99QXVHPzBhSKmN9PPhg8d8pUtO5F4l6amr+\nOqxFUYSox1V1HEBEhlX1J8CR5X/KHYpYH2Nj5WY7tSPrFqUTihqqpeilXWBcKOpuWh91UsUg/XjW\nraDXC4q6ClHnWTZQ/SJTZoxD+RS9binqZcvM+1WJuuqiAVCMqG8XkVXAF4Cvich/AxVXOnODIul5\n4C+NphOKGlopemXQCUXdrayPsbGW/VIWSdYH1C/MlHc8fSrqOtaHT0Vd5CIQR3wBgSLICyaCn2Ai\n1Jv04pWoVfW5qvqgqp4N/CXwb8Bvlf8pdyiiqKE+UWd51AcOVCtDWpaob7mlXO5zt9PzfFofddpP\nsz58K+rBQTNOfQQT61gfPhV1kYtAHGWJulvBRGhNeqnaLniwPkSkX0R+Yl9HRZkuUNWK88/coBNE\nnXVVtZNeqqjqstbHzEy51V66paiXLDHBuapEmhWohPrphWn7pa5HXeR4ViWNvMyJgYHqBciK9Lvq\nmClrfWzaZLbFtfVRdbJOVsCvJxW1qs4APxWR7RX65Q2dUtRZ1gdU86nLKmooZ3+k9Xtw0LzvIusj\nKZgoUq/eR1FFXYeofVofWcq06hqYRTInqtakLqJ6+/tN+1UVdVHro7/fzFAsmks9Nmb6nVUP3k7W\nqTIefRF1HUVdpArvauA6EbkK+PWQ6ObitgcOmCtwWiqXb6LupKIGE1B88pOLtZ/V77r1PiYnzcmR\nNgGjztT9okRdp8BRmvWxZ485qdurpRVBEcLbtKlare4ik0aqZjgV9ZGrBHHLKmool6JXJBc5HsRN\nWs8yDdPT5pFF1HXLp7peOMDiL8s36xdZJU7Bv0fdKUV90EHmglRWUaf1u24FPVu/IU3JdIKoXSvq\nuPJaWaEmZJHjuWVL+eL4tm07ltNQdfGAoj5ylTzzssFEMER96aXFvlskFzlO1AcdVLwfef73+vXm\nO/v3l184xHfWx6kJi9ueWv6n3CFvdRUXy3H59qjT7gbiGBgwqUtlUvR8K+qsk69O/noeUW/aZJ5/\n+MNq7WcpavBXihTMKiO/+lV5v9Sn9VFGUfsOJoIZ53fcUSxwXlZRl0FeRkmdXGpvwcQIT0t475Ty\nP+UOaau7WPhOz7PrzFVV1Hn+WhxlcqlVs/vtSlGnwaei3r4dnvY0+NCHqpVSzcqjhuoBxaKKemKi\n/G8UJWrfirpT1sfMjLmg5aFI8f2qRJ2neusQtRdFLSKvEpFrgSNF5Eexx83AteV/yh3yiLqu9ZFH\neAMD5gSvQ9RFUSaX2p58WURdN5jYLaIGeMtbzOrsn/xkubbt8UyzPsA/UUMxEmpvO2+sLFtWbZ93\nQlGXtT6gmE9dpPh+LxK1L0X9SeA04ILo2T5OUNU/KP9T7pBH1HUXuLVTPbOufFUnvZQl6kMOMYOi\nyLbkDYS607AnJ7MtGxfWR1b7T3saHHssvOtd5WyE6en0qbuurI8sUvJJ1HXGIeS338lgIhQj6qCo\nY1DVPar6S1V9AXA7MAUosLzb6Xp5RD00ZB5VSaNIQv3mzWZRzrKooqihmKrOI+q6irqb1gcYu+gt\nb4HrroOvfKV421n7pa71UcRC8EnUW7aYu4zZ2XJt+wwmLgRF7WPhXK/BRBF5LWaxgK8BX4weF5X/\nKXfII2qop+6K3KIccQTceGP5tqsSdRGfuoii7nWizguynnEGbN1qVHVRJK1AbuHC+hCZu8RXOzZv\nNs++iHpmprzCK2N9HDhQbnZsFUW9bJm5OyiSS10kmLhsWbXFA/LIdMUKk0zw85+Xaxf8BxPfCByp\nqrtU9djo8fDyP+UOeel54J+oDz/cnHhlI+5VrA8oRtR5amDFivInXRxFsj7Gx6u1PzmZXSTfYmgI\n3vAG+J//ge9/v1jbSSuQW9RdtqlIcHjpUqPcyxJ1kZrOVdV6GUUN5eyPKkQNxXOpi6Tn2XU2XWd9\nABx9tLmrKwvf6Xm3ATXXaXaLvPQ8MKRRVd0VVdRQXlWXJeo1a8yAc2F92JOujo+cp6irtl90jT2A\ns84yv1VUVWftl6EhQ6R1FHWR47llSzkynZ01sZK8trduNc933FG8bSinqKEcUVexPqD4AgJFi+9X\nCYQWIdNduwxRV1nPdGio2sSqIv9yE3BJtIbim+yj/E+5QxHro8505iIHyxJ12VugskQtUjxFr4hH\nDdUDilNT2dZEncJMZYh6dBRe/nL4zGeKn9iQXYq014i6KNnVVdR57VeZEVpHUd9ySz4BFgkmgl+i\n3rOn/D4v4q2noQhR34rxp4eAFbFH19ALHvXhh5tn30QNxVP0ihJ1HR85z/qAaheCvNS/drzhDeb5\n/e/P/26W9QH16n0UXRuwLFEXJbuNG83FvIr/XcRq6qSi3r7djM28Y1GU8HwSNZS3P6qu7gLFypye\no6rnYBa5PSf2umvoBaJevtzMlvNtfYAh6ptuKqY0IN/6qKqo84jaFoAvu9hBkbbbsX07/N7vwXnn\n5avhPEVdp9Rp0bUBy2ZnFCXqwUGTieAjUAmdV9SQf5fUCUWdde5XJeqq6yVCsayPx4nI9cBPotfH\nicg/Vvu5+rADvdtEDcb+6ISiPuQQ06e8wj55gRDfivroo81zlUBLWaIGk6q3bx/8y79kfy9PUXfK\n+piaKp7zXIbsyqp1KH6BqaKoy5RJiKPISi923UFfirpIMHH9evPoKUUNvA94BnAfgKr+EOjaKuSd\nJOq8ndopoi6aS11UUfsi6tFRUwCnU0R9/PHwlKfA+96XvfJL3n6pa30UOZ5lU/R8E3VRy6aKos4r\n3pUGq6izUvTyZt/G4cv6gFZAsQyK3gkkoVD8UVXbS9fXWI2wHsoQdVVCKnL7A4ao77qr3O9UVdSQ\nbyl0IpiYd3JXGcBQjagBzjzTkFTWvvFpfZRR1GDsj6LtQvG2y2Z9+FbUZcc4GAtnaChbUZdJcauy\neMDYWLHMjF274Prry7ftM5h4m4g8HlARGRSRtwA3VPu5+rBEXSQ9r+oCt0WtDxtQLONTVxnEO3aY\n515Q1Hm3s8ccAzfcUH6/VyVqW3I2i0iKWh9VVgMpS9Q+FPXWrWamXJn1JIsq6pGR8hNHqh7Lvr78\nXOoyk0ZGR804LDPXoag9sWuXGXNlLpC+rY9XAq8BtgJ3AI+IXncFRRW1VY9VSkCW8ajBP1EvWWJO\nxm4r6iIn4K5dph9lA4pVT+4iF58i1sfkZOt7ZVCU8MpaH2VKhdqLQJnFCYqOQ5Hy9T6qKmooTtRF\nFTWUu8gUVb1V4jFeg4mqeq+q/oGqblTVDap6pqpWXIO7PspYH1DNpy46GKqk6FUdxEVyqfMIaXDQ\n/LYvjxqqR8SLqPUkFLn4FLE+wM+6hhbDw0b9+/KooXyedtFxWHaGX9GLVxJsLnUaitqSUI2oi/rI\nVca5V0UtIh8VkVWx16tF5MPVfq4+OkHURQfDsmXmJOkEURfJpc4jJKhX76MIUVfN/PCpqItYH1DN\npy5zPMsE/cqsklKFqMuQaVlFXeYi0I7Nm+Huu9NtqE4o6iJtr1tnPPWeIWrg4ar66yGsqg8Ax1f7\nufropKIuMpDLZH6oVh/EhxxiqvXZEzgJRRb9rLN4QJFg4vLlJs2qU0TtUlG3E/WePfDRj2Z7v76J\nuqmKuipRr11rasWkXXh9K+oyZFo2cO7V+gD6RGS1fSEiayi21qIXlCXqKurR7tAi6UWHH17co66y\nRJHFoYcaos/z7/IGQl1FXcSeqJL50QlFXcb6+NrXTO3rF784u6RqLxD1+vUm4NdLirqq9WGDw2n5\n5mWDieCXqMtkfvhW1O8GviMify0ifwNcAbyz2s/VR6cUddEdesQR5latyECuOmMLipU7LULUVRX1\nzIx5FDkBd+2Cn/ykXBW9qif3kiWGpPIUdVbKVVxR79sHr341PP3prbGWNUmlLFHfdVexjJgyY6Wv\nz1gGZTIQyijqsvnIdRU1pO/zsul54CeYCGac790Lt7UnL6fAdzDxY8DvYGpS3wX8tqqeX+3n6qNM\neh5U96iL7tAymR91iLpILrVPRW1v/4uQ6THHGCIokw1TttaHhS1nmZf1kbXP7Qn9la/AccfBuefC\nm94EV15p3s860cso0zK1o8uOla1b/SnqsisD1Qkm2vVIu6Woy4i0MgFFuxyc1wkvmOnjn8csy7Wv\nmyu8lE3Pq6qoyxJ1EZ+6DlFv3mz+Lyug6FNRlym0UyUiXud2OW+b0lYgt7CK+iMfMc+XXgrvfndr\n5fOsIGMZZVrGSy47VqoUfSqrqIve4tcJJha1PooQ3vLl5m7Dp/UBxv7IQ5l+J6FI1sfraK3wchFd\nXuFldtbs/CJBLfBP1IcdZp59E7WdDJCVulSUqKuWIYViZLpzp1G6nSLqIoo6a7+MjMApp8BrXgM/\n/CE86Unm/YEBc+eWRtSq5QivTC61b6IuG0ycmsoOZMfRCeujyPlZZfGAMkS9Zo25mBcZ53VWd4Fi\nQcE3YFZ4KZU7HaXwPRu4R1WPSfhcgPcDpwIHgBer6vfy2p2dNWo6L9DXKY966VJz2+nb+gATNMry\nS4v0uxPWx9KlxqrpFUWdZ32IwMUXJ3+WVQfELppbVlEXmUZehagfeKA40ZQNJoLZD0WIps6xXB2l\nLdx/f/LnZZVpWX+9bMCv6GovdVZ3Ab8rvPw78MyMz08BjogeZwH/VKRRS9R5sAvcVrnNLzsnv2iK\nXl2iXrsW7r03/fMiinp01MzWLDsLr+iahhZlMz98Kuo86yMLWZX1yh5Pa6UUUb5lM4TK1hIpq6ih\n+LlUR1EPDJiLowtFDeULbpU994tmftRV1N5WeFHVy4CU6yIApwMfU4MrgVUisjmv3aJEDUaBVlkt\nuGx0tlNEvW5dvqLO6/dxx5nnq64q99tli8Hv2gU//Wnr/4q071NRVz1Bsk70ssezTO3oooX9LSxR\nF838qKqoi6DOsQQjSFwEE6G8oi4b8Nu1y9y159XQ7oSi9rXCy1aMWre4PXpvHkTkLBG5WkSuHhub\nKEzUmzaVq39gUYWod+/OHxAuiPree7NnbeX1+0lPMrf6l15a7rerEPX0dLEL2Oys+a5Pj7rqPs+q\nrFdm9qBFUS+5bOaEXTuxTJ52LypqMN5vlqIeGMhe9T2OMkQ9M2MsvrJEDfl3j96JOraiy7uBd3dj\nhRdVPU9VT1TVEwcGhnNT8yw2bjQ5zmVR9qpatOaHC+tjfNyscJOEIkS9ejU8/OH+ifqYKCpRxP4o\n438noW7WRxZcWh9QjqjLtgvF2i6TEw/lFXVdos5T1GWOZRmirkKmRYnau/UhIseIyPeB64DrROQa\nEdlV7efm4A5gW+z1QdF7mShjfVRV1FU8asgPKLpQ1FB/EJ90ElxxRXFbAsqT6VFHmUyVIkRd1v9u\nx8qV5vYzbZmruoo67USvMtPUF1GvWmWOvQ//u6yi9m19lCFS30S9erXJ5um6ogbOA96kqger6sHA\nm4GcxY8K4QLgRWLwWGCPquaGQsoQ9caNxqMuuk6dRdmrdtEUPVdEnRZQLEPUY2Nw9dXFf7ssmS5Z\nYvZLGaKuo6hV00vaulDUSXZTVUV99935szarrFZf9CJQlqjLKOqyKYtJWLs2PeujrIgqkwNeNlBp\nYQOKWfCeRw0sU9Vv2heqegmQaz6IyKeA7wBHisjtIvJ/ROSVIvLK6CsXYwKVN2KI/9VFOlxWUc/M\nFF+nzqIsUY+MwLZtnbE+IJ2oiw7i34gWUitjf1Qh06KZH3WJOq/eR91g4vR06ySOoypRq+ZbclXI\nroxah3IzE6GYorYXoLqKes+e5ItZFUU9M5NuF7a3DeXJ1BJ1liCsehGwKJT1ISJ/KSI7osdfYAg2\nE6r6AlXdrKqDqnqQqv6bqp6rqudGn6uqvkZVD1PVY1W1kL4rS9RQ3v6oMtXz8MObo6jXrTOD65JL\niv92VaL++c/zJ0q4UNSQTiR1rQ9I9qmrEjXkE6pPoi6rqAcHzflQRFHXHePQEiRJqrqKooZifa9q\nT+zaZe7mXC0hloQiRP1SYD1mCvl/Auui97qCstYHlAsollnlOI4iKXo+PWobsS7a75NOgm9/u/jy\nTVWJembGpOlloW4wMU9R17U+oDlEfccd+bf5VbJVis7wq9J2O7LqfVQJJoJ/oobsu0fvwURVfUBV\nX6+qj1TVE1T1jVFN6q7At6KemjIDvQpR33efmR2WhrpEvXq18SKTFHVecfx2nHyyUQHfy50LalCF\nTItGxDuhqOtYH5B8oldNzwM/RL11qzmmeTNPq4zDoqVO65TytciaRl525mAniLrIYhneFbWIfC1h\nhZeMCr1+MTubXznPooqirnrlK5L5UZeo+/sNWScRddl+l/Wpq2RmHHmk6bNvoi7iUVfd51mKugop\nbdhgsmHyiLpKYaOiF4Eq/S6rqH0RdScUddlzf9Uqs++LKGqf1se6hBVeNlT7OTcoqqhHR82AKaOo\nq+7QIrnULgZx2uzEsv3euNGk0JUl6jJkOjxsLmDdVNSzs+UsoXYUUdRljmd/v9n3eVO9q1ofUEyt\nQ7n9XVZR1w0mQrJHXSWYCMWIug6Z5gXO7UWg6rlfhKhn42VNReRgoGDBQz8oStQixv4oo6irXlUP\nO8z8Xh5Rl5kWnIS0eh9V7gROOgkuv7xYIfuqJ+CuXfDjH/tp2yJLUde9OLoOJkKxoF+Vms4+FXXR\nfGTfiroXg4lgxvkNN6Rnfth+F1k1KglFiPqtwOUicr6IfBy4DPjzaj/nBkWJGox6qaKoyxL1kiX5\nKXp180uhNY28HVWJ+qGH4Ac/yP9uHaL+xS+S09vqtm2RpajLevftcB1MhOJEXbbdomVUqwYTy6xi\nVEdRL19upoin3Tn2mkcNZpwfOAC//GXy53XiJFAsmPhl4JHAZ4BPAyeoatc8aihH1GVnJ9aJzh5x\nRL5H7YKos6yPskQNxeyPOkStapbmct22xZIl5sROUtR1o+0jI8aXd2V9gD+iXrHCPLqpqF0EE0XS\nZyeWVdRlFg+oQ9Q7d5rntAynOuslQsEVXlT1XlW9KHpkFNrsDMoq6irBxCo7NS9FzwVRW+ujPQWr\nCiFt2WK89SL51FVT6IpkftQlapH0eh95K5AXaTut3kdV9bhliynilTWFv+pYsSl6WaiqqPfuzZ/l\n68L6gHSiLqtMyyweUGdSyraoGEbavq+zDBcUX4qrp1BWUe/eXXyh1ToH6/DDTQAkbfqrK0WdVJip\nqnI8+WT41rfyfeqq9TiOOMKo3SJEXbXWB6RX0KtrfUB6vY+q6tF6yVl3elXHSpG1E6um56nmL8Th\nIpgI6dPIqyjToncDdUTa5s3mopBG1FXmZsSxKIhaNbvgfhx1rQ9IV9WuiBrmq42q/T7pJKMWr702\n+3uTk2Yglg2EDg2ZND2fihrSPdS61geklzqto6ghm1DrKGpf6XmQ71P7VNTT00ZQlD2WRYl6bMyI\nhSrBfltrPIuoF52iLppHDa1c6qI+da8TdVq9jzpEDfk+ta2IViVqnZf54YKo09aCrGt9QLb1MTho\nPNAy6ARRZ81OrJqeB8VrrrtQ1GlixJeirkumW7dmWx9eFLWIHCsiV4rIbSJynoisjn1Wcn0Qtyir\nqKG4T13n9ufQQ83zL36R/LlLRd1O1FUtm23bzPqGRYm6Cg47zCzKm0YePhW1T+ujDplCOlHb3O+q\nbU9Opttv4FdRuwgmQmvxgPiYqTrGe4GofSrqfwLOBo4FfoZJ0YsKelLDTayPssFEKK6o63jUw8PZ\nSy31ovUBRlVfdll2oGhqqjqRrlpl2k4rQ9oJRV2HqLMUdZXjuW6d8e3TxokN3Pq4CEBnFLWLO8eJ\nibmxmE4o6jrjpFtEvUJVv6yqD6rqu4DXAl+Oakc3YsILlK/3UffEzoq696L1AYao77svu6bu5GT1\nYJ89ydPUWN2iTJDvUdfZ71mKukqf+/pM8Cnrgg7V2i6ydmIVMi2rqF1YHzBXkPhW1HUzM7ZuNedl\nUrVIr3nUIjJq/45qUv8OcD5wcPWfrI8yRL18ufl+Weuj6k7Nirq7IOq0wkx1iRqy7Y+6q4RD/kop\nPtih3k0AACAASURBVBS1K+tj//75lQar1OOw2Lw5fRp5HVVaRFFX2d/dUNQw18KpOsaLLh7gwvqA\n5H3vU1G/A9gZf0NVfwQ8BVPytGsoe6DKTHqpWzzFt6IeGDBk7SrQArBjh/Gqs/Kp6xB1nqJ25VEn\n5fm6CibCfJKqczyzsjN8E/XEhBlHZYKgvaSoq1gfaYs/xOGKqJPOf2+KWlU/qapXAojIchFZHr1/\nq6q+vPpP1oNI+Sh7mXof9mBWHWhbt5rlv5ImM7ggakiu9zE+bvZL0dWZ4xCBE07Itz58Kuq+vno1\nUOw08nYf3JWihmYQ9fCwGR95irps28uXm3HSaUUdJ+o6ihry++6TqL2m54nIq0TkVuAW4FYRuUVE\nCi2Z5Qs2OFj2f8oo6jrFU+zBSvo9V0SdVO+jbr+zFnEF/4q6rgJLU3yugokwP6BYl6jvv7/Vv/Z2\nwc9FwLZfdn/39eWv9m7bFqkmGOJIWjygTjARFihRR0tunQacrKprVXUN8JvAKdFnXYHdGWVQ1vqo\nc1JnBXNcEnWS9VGXjLIGcp2sj7wTxQVRW0Xd7lO7CiaCe0UNyT61b6Ku6q0XCcrVybePw3UwEfL7\n7uIcWrp0/rlfdvWlJGQp6hcCv62qv14fMfr7d4EXVf/JzmPjRqNesmorWLiI/IJfok6zPure3u/b\nlz6VvE7WRxHrw5eidml9JCnqqv3O8pJ7UVFDsQp6rsb40JCxW5qkqEWSU/Tqxr0gm6hVVefdmKnq\nGJBTmqW3YFP07rkn/7sucikh/QT0bX1UhU97Im+pLN+Kuqp3b5FmfdTJ+vBN1HfdlX7RrToOyyhq\nF2iv9+FbUdclasgmal+K+g4ReUr7myLyZCBnfYreQpnZiXUJb+1aM1B9Wx/thZlcEbUP1dvfb9RR\nVtt1CjJBtkc9PFzvVtyn9ZE2TqBe2zMz6cKk6gWmk4oa5k8jrxtMTJq0FIcvoq67XiJAls54PfDf\nInI5cE303onAE4DTq/9k51FmdmJdwhNJTtGbmTEPV9YHGFW9PVp7p5eJ2rbfDUVdZwXy9rZdBhPX\nrDH7JKkuTN1p2PG7OruYQBxVrY/RUbjppuzv1LGD2tFO1HXS86Czilq1JQ5cEHVWet51wDGYFV12\nRI/LgGOizxqDsoraxcFqv6V1lbYEyfU+6lo2eYO5TjDRtt8Nj7ruBQzMHUFSTeM6RC1iis3fcMP8\nz+oWNsrLpa4TTCySR+1KUdt6HxZVFXWR1EIXAT8w5/7k5Nxz04X1kaqoReRwYKOqfrjt/SeIyF2q\nmlJ6qPdQRlHXJTwwJ8oPfzj3PR9E3T6IfSvquvWiu5H14epWPKnUad22jzoKvvzl+e+7sD4gO0+7\najCxSB61T+tDpFpqYV7fXQT8YG4ywfr15m+vihp4H5B0/Xwo+qwxGBkxB6oT1gf4V9RJ9T6abH3U\nVetg9uvgoB9FDcmFmere5u/cacZkUrtQfaxs3GgIzYeiHhubP5W+vW2X1scDD7Rmm9ZZIDYvEOqC\nTCE568t31sdGVZ1XTj56b0f1n+wOis5OdHFib9liUt3ipOHb+uh1ovatqO1yXElZH64UdXv/697m\n23X22u2PumNlYMCQdVYpg6qKGrLtD9eKWrV1IatjS3aTqOtU5LTIIupVGZ/V3JzOo+jsRFeKGuYe\nLJdEbQszddr66OVgIiRnJbgIJkK6onZB1O0L/7oYK9u2wW23JX9WJz0P8onapaKG1jivY0t2iqg3\nbZq/JJdv6+NqEZlX00NEXkYrC6QxKKqoXUV+Ye6tp0uiHhgwCq9dUdfp95Il5gTzFUz0raghXVG7\nIOp2RT07awr91Dmehxxi/t+1ogaTDXTrrcmf1UnPg/xSAy6DidAi6jrHsihR1x0rg4Pz72a8BhOB\nNwL/JSJ/wNz0vCHgudV/sjvopKJOypF1SdQwf9KLi377zMwYHTV531NT84OSPhX1+HhLCdZBezDR\nxfHs74eHPcwPUW/bZgKV8TSxePtV0/Ogs9YHzFXUdayPpAwbC1fBRJifS+01j1pV7wYeLyK/iUnT\nA/iiqn6j+s91D5s2GRLKI7QmWB/QHaKuk/VhT/K9e1tKKd62K0XdXgPFpfVhaxqLuDueRx0F3/ve\n3PcmJurPpty+3VQSfOCB5P3tU1H7sj46oahdEfXNN7de+w4mAmbBAFX9YPRoJElDsVxqVTeEt3Sp\nUWC+rA9ITl3yRdR2sk5d6wOS2/etqF1ZH7OzJkgM7o7nzp3mpI5X0XNhH9iJUEn2R9MUtZ1G7iKY\nmLZ4gGui7mQwcUGhSC711JQ5GV0crPbZiT4V9fS0IVJfRO1iqaysk9y3R+1in7dPQ3a12vbOnWbM\n/exnrfdckN22beY5KaBYtf0iitplMHF01NxZuAomZi0e4Jqo77+/1abvYOKCQhFF7cL0t2i/qvok\nalf9TiNqVyuwQOcVtSvro73eh6vVtpMyP1yQXZairpNHDZ0LJvb1zZ2dWFdRQ3rfXQUTYX4ygZ2o\nU8c6XDREXURRuyZq39aHLczkm6g7oajrFmUCo6j37Zu7HJfLPGqYr6jrtv2wh5mTOB7ocqGoN2ww\nx6udqOvYWGmTiuJwaX3AXKKuq6ghnahdBxOhJdRsELROYbBFQ9QbNpjnTinqLVtMUXhbatKHogaj\nqjulqF0EE30ramj5yOA2mAjuiXpkxKTpuSbqvj446KD51kedOwGR/KCcy2AizI3F1A0mQr6i9kHU\nLuoHLRqiHh42E0WyFLXrgzUzA7t3m9c+idrVbdvoqPF42+sYN8X6aK/34So4DPOtD5fH86ij3BM1\nJOdS1/XWs0qdqrq1PmA+Ufu2Pnwp6rpjcNEQNeQvyeVaUUPrYPmwPsAMYpeKGuYH5FwQdZr1oeqm\n1gfMn+I8PW1sEJ/BRBdt79wJP/3p3LsvX0Rd11vPUtQuLLJ2xBcP8Gl9uPSoV66EZcvmWx91sOiI\nupPBRPBH1L6sD0iuaQH1TsAlS0xecHvb09P127ZoV9QuluGyaN83rrI+wBD1xATcckurbRfjZNs2\nEyex+9i2DX4UtesxDp1V1AMD9RflhflLcrm4q1tURJ03O9EHUduAYpOJ2oVSsv5m+0nu4iJg0a6o\nXR7PJUvMwypqV1kfML84k0tFPTMzdwHduuMwS1H7Iur9+w2R1ok3FAkmurA9LOJEHRR1SeQpapc+\n1YYNJqDTrqhd3RauXm2e49ZH3X7nKWoXy2X5UOsW7YraxQrkccQLM7m2PsAPUcNc+8OF9ZFVXAvc\nWh92VqUVPFXH+IoV2YsHuCDTOAJR18DGjSYjYP/+5M9dKrCBAXNhiBP10FC9FJ329levbo71Ydv3\nSdTtitql9QFzCzO5roa4cWOLqF0F5JImvbiwPjqtqKF1HlU9ln19hqw7SdS/+pWJkQTroyTyJr24\nJGqYm0vtOr8UWpNemkTUPq2PNEXt6nj6UtQwN/PDpUcNfhR10lRsH4raEvXtt5vnuqV8O0nUU1Ot\nrKygqEvAEnWaT+36xI5PI/dB1DbQ0hSi9m19pHnUrva7L0UNrfUTVd1Nw1650vQ5TtQuFHXaVGyf\nirqu9QH5RO3qvIe5yQRBUZdE3uxElx41zPWpmqyoXaVd+VbUduacj6wPmFvq1HXMYedO0/Y997gd\nK9u2JRN1HUUNyT61630C7qwPyCZqH8FEMP0OirokumF9PPBAK2Ld60S9ZIkhuqYqavsbvhR13Ppw\nmfUBcwOKLsfK9u1zPeq6+ztv4hK4n0IOLevDp6IORN0jWL/eBPM6aX2AuW1rgvWRNkXYVdZHkr/p\nqm2LlSv9Kmqf1gf4IepOK2qX43zpUnP8fCtq10S9aVMr6ytYHyUxMGBUaJ6idjXQ4rnUvhT12Fhr\n5parGXg+g4nt/qZrRb1ihZ88ajD9Hx83x9JFcf84tm6F5cvdE/W2bWZ82Eynuqq3iKJ2aX2AESRN\nU9TxBYaDoq6ArGnkdZajT0J8GrkvogYziF3NqvJJ1EknuQ/rw1cedbzeh+vjKWIyP66/3m29DJtL\nbe2Puj5ypxU1GKK2k3ZcKOqkjBXXwUQwF99bbjGTjgJRl0TW7ERXBXws4j6VL+sDDFG7VI0+FTXM\nPcl9Kmof1gcYn9rH8dy5E370I/O3a6K29odPRe0jmAhmnNtp8HWJempq7mo6Fq6DiWDO/1/8wvwd\nrI+SyJqd6JqoR0eNx+bT+gD/RO0q6yPpJHddyCdJUbvcN9AiateEtHNnq9qiS+sDWkTtU1H7CCZC\nS5BAPTK1F9r2dTXBvfUBhqjtfg+KuiSsok66/XFN1CKtXOomE7XLYCJ0TlH7tD5cl/OEVkAR3MZJ\nROZbH1Xbt5OKshS16/0SX5y3zjg/8UTzfMkl8z/zRdR2EYugqEti0yZzAreX8gR/B8u39eHyArOQ\nPGrX1ke7ovZJ1K72x+CgEQuurI+BAVPCM0tR+7A+LOqcnyecYM7/Cy6Y+/7MjOm7j3PfIijqksia\n9OJaUUNrGrmPEzuuNFwNMrt4QHw5q8lJo8r6++u3DX6J2i7HNTPTrGAiwKGHtu5aXLYdn/RiL151\n7o7S6n34DCZa1FrJuw9OOw2+/OXWuAO3y3DF0RiiFpFnishPReRGEfmzhM9PFpE9IvKD6PE2n/2B\n7EkvPojap/VhCzOBW9WoOveOw67AUjcbphPWR3w5rokJQ0h9jka572Di4CAcfrj522Xb8Ukvdn3K\nOvskrYJeJxR13XF+2mlmbF96aes917EMizhR96z1ISL9wD8ApwBHAy8QkaMTvvotVX1E9PgrX/2x\nsMGVn/1s/me+FPXEhLkwuD6xoTWIXd/etwf8XBZN8q2owZyMro/n8uWG4HwRNbTsD9dEfeut7uqI\ndEtRDw3Vv+g+5SlG3V54Yes916UjLJqiqB8N3KiqN6nqJPBp4HSPv1cIRx5pduCXvjT/M18eNRiy\n83Fi24CiT6J2taZhkr/pS1E/9JC7Fcgt4jM3fWR9gD+inpgwGSUugqBr1rSKJMXhehKQhUsxsnQp\nPPWpxqe2CQW+iHrlSnNxhx5W1MBWIL4G8u3Re+14vIj8SES+JCK7khoSkbNE5GoRuXq3zV+qCBF4\n1rPgK1+Z61OBP+vDoslE7XKKd6cUtasVyOOw9T58ZH2AH6KO16V2cYF52tPg2mvNOo9xuF6B3MLG\nYlwR6XOeYyai/PjH5rUvooaWUOtlRV0E3wO2q+rDgQ8CX0j6kqqep6onquqJ69evr/2jz3628TAv\nu2zu+76sD4umWh8uT8D2rBLfitr18bQV9HxZH099qhESxx3nrs34pBcX/X7BC4xy/sQn5r7va5+4\nHuPPepZ5ttkfi52o7wC2xV4fFL33a6jqQ6q6L/r7YmBQRNZ57BMAT36yGVBf/OLc932c2Js3t/5u\nsqJ2SdRJ1kfdjBKLdo/a9T63hZl8kdLGjXDRRa3j6gJxonZxJ7B5s7mgfPzj8wts+VDUNmDuikg3\nb4ZHP7rlU/sKJkKLqHvZ+vhf4AgROUREhoAzgDkZjCKyScTkEojIo6P+JMwbcotlywxZX3jh3IHm\nw6MeHm6ddIGok60Pl0uUxRW1T+vDF1H7wNq1Zj+4sj4AzjwTbr4Zrrii9Z6vfTIwYC6QLo/laafB\nd79r0nQXtaJW1WngtcBXgBuAz6rqdSLyShF5ZfS15wE/FpEfAh8AzlBNmjPoHs9+tpmHH8/+8KGo\noXWwmnBb6DPrw7bfrqhdqjCfWR/gX1H7gEgr88OVt/7c55rA3Pnnt97zuU/WrnVLpM95jnn+4hf9\nEvVv/iY86lGt1M6q8OpRq+rFqvowVT1MVf+/6L1zVfXc6O8PqeouVT1OVR+rqldkt+gO1qey9oeq\nP6K2AcUmKOqREaNgOhVMdHkRgBZR+8j6gLmK2sdtvi9YonbV7+XLDVl/9rOttDxf1gfAhg3mTtgV\njj3W7JMLLvBL1E9/Olx1Vf3zp9vBxK7h4IPhmGOMHwimOtfsbPMUtWuiTlo8wLdH7fLkHh427fnK\n+li1yvR/bKw5ihpM5oe1Plz1+8wzzQpGNtXVp6L+wAfgne90156IUdVf+1qrnrsPonaFRUvUYOyP\nb33LkFInfKomWB/gl6hXrmxN8Xbddvw3fClqewu7f3+ziHr7dlPTed8+d/1+6lNN8NPaHz4V9Ykn\nwvHHu23ztNPMeW/vqn2INFdY9EQ9PQ1f/arfyK9P62PDBvNsb/ldwLeihpaq9nFyr1jhN4/aomlE\nrQo33eRufw8MmFS9iy4yyrpJvj3ASSeZsfL1r5vXQVH3KB77WJNMf9FFnUnR8WV9XHihuQ11hXai\ndh1MBL9EHVfUPqwPiyaRkp30smeP236feaY5hp/7XPN8++FheMYzWosSBKLuUfT3wymnwMUXt9aU\n80HUj3ucUe8nnOC+bTBt21xTF/BtfUCrfZ+K2qf1Ac0iaptLDW739yMfaZYQO/98f7M1fcJmf7ha\nys4XFjVRgyG5e+9tzVL0cVVds8aoXltitdeRRNSusj46qaiD9dHCttjUM9f1T174QhPrufnmZu0T\ngFNPNbMse1lNQyBqnvEMo6w/9znzupcDCp3CQlLUPq2PJt3mL106twqdS/z+75vn++9v1j4Bs0+e\n8ITeP+8XPVGvXm0O1De/aV73+gHrBGwKnV08wEcw0SdR21xtH7fiTVXU0LI/XPd7xw540pP8tN0J\nvP3t5tHLWPREDcb+sOligahbiwfs22deNy2YuGJFawHTYH204Iuowdgf0DxFDfDEJ8LLXtbtXmQj\nEDWGqC163avqBHyq3iTrw5X/Hf8NG8n3sQqLnSHXNKK2PrUPMn3e88z+cJkmGtBCD8c5O4ejjjLr\n1d10U1DUMJeot21zS6ZLl5qYgG9FbeHjeI6ONm/CC/hV1KtXm+Wt4tklAe4QFDWtxQQgEDX4VdQi\nc+t9+PKoLXwcTxtQbCpR+7InHvOYuWV9A9whKOoIr3mN8WTjaUyLFXGinpkxD5cnd7zeh+uiTDBX\nUfsgU7t/mubH2rHdtAtMQCDqX+PII+HDH+52L3oDcaKemjJ/uySloKi7g0MPNTnDdUtuBnQegagD\n5sE3UcfztJvoUTeVqDdtgv/9Xzj66G73JKAsgkcdMA9xorZLZbnMzIhbH74VtU/ro2lEDWbKd4jD\nNA+BqAPmwWZmxIm6SdZHUNQBCw2BqAPmIb54gA+itopatZkedZMVdUAzEYg6IBE+idoq6ulpQ9ZN\ny/o4/ng45BBTbCsgoBMIwcSARFii9hVMnJpq+dSuiXpoyBC0j+p5YAp53XST+3YDAtIQFHVAInxb\nH2DKy7pu28LaH8GeCFgICEQdkIhVq/xlfVgS3b3bfdsW1v4IGQ4BCwGBqAMSsVAUdSDqgIWAQNQB\nifAdTAS/RG0VdbA+AhYCAlEHJMKm0PlU1Nb68KWoRfzYKgEBnUYg6oBEjI6aFV7uv9+8bqKiXrLE\nkHVAQNMRiDogEe2q1/UU8njbvhR18KcDFgpCHnVAInySaScU9StfCY97nPt2AwK6gUDUAYnwSdSD\ng2bJM5+K+vjjzSMgYCEgWB8BifCdQjc66ldRBwQsJASiDkiEbx955Uq/ijogYCEhEHVAIixR33OP\nefahqPfv99N2QMBCQyDqgET4zPqItw+BqAMC8hCIOiARdvGABx4wr33WjA5EHRCQjUDUAYmwiweo\nmr/7+922H1fUYfZgQEA2AlEHpMKS6dCQ+xl+QVEHBBRHIOqAVMSJ2lfbvtoPCFhICEQdkApLpj6s\niUDUAQHFEYg6IBU+FXWwPgICiiMQdUAqOmV9hGBiQEA2AlEHpKITinpwMJQiDQjIQyDqgFR0QlEH\n2yMgIB+BqANS4TOYaBV1IOqAgHwEog5IRVDUAQG9gUDUAakIRB0Q0BsIRB2QCp9kumwZ9PUFog4I\nKIJA1AGp8EnUIsanDkQdEJCPQNQBqfBtT6xcGXKoAwKKIBB1QCp8Zn3Y9oOiDgjIR1jcNiAVvhX1\nqlUwO+un7YCAhYRA1AGpWLbM1KH2RdRnnx2IOiCgCAJRB6TCBvx8WR9PfrKfdgMCFhoCUQdk4m//\nFo49ttu9CAhY3PAaTBSRZ4rIT0XkRhH5s4TPRUQ+EH3+IxF5pM/+BJTHK14Bj398t3sRELC44Y2o\nRaQf+AfgFOBo4AUicnTb104BjogeZwH/5Ks/AQEBAU2FT0X9aOBGVb1JVSeBTwOnt33ndOBjanAl\nsEpENnvsU0BAQEDj4JOotwK3xV7fHr1X9juIyFkicrWIXL17927nHQ0ICAjoZTRiwouqnqeqJ6rq\nievXr+92dwICAgI6Cp9EfQewLfb6oOi9st8JCAgIWNTwSdT/CxwhIoeIyBBwBnBB23cuAF4UZX88\nFtijqnd67FNAQEBA4+Atj1pVp0XktcBXgH7gw6p6nYi8Mvr8XOBi4FTgRuAA8BJf/QkICAhoKrxO\neFHVizFkHH/v3NjfCrzGZx8CAgICmo5GBBMDAgICFjMCUQcEBAT0OAJRBwQEBPQ4AlEHBAQE9DjE\nxPOaAxHZC/y02/3oANYB93a7Ex1A2M6FhbCdxXGwqhaawdfEMqc/VdUTu90J3xCRq8N2LhyE7VxY\n6PR2BusjICAgoMcRiDogICCgx9FEoj6v2x3oEMJ2LiyE7VxY6Oh2Ni6YGBAQELDY0ERFHRAQELCo\nEIg6ICAgoMcRiHqBQkSk230IcINwLBcWqhzPRUHUIjIsIiu63Q/fEJFlIvIo+HVlwgWHcCwXFsLx\nLIYFT9Qi8jrgKuDDInJmt/vjGVcC77KrvYvIgjq+4VguLITjWRwL7uBbRFfq9wDPAp4DfAR4s4hs\n6G7P3ENE+kVkAPg+ZuCfAaCqs13tmCOEY7lwjiWE4wnlj+eCI+popwBMY5b6ep6q3hItYnAXZkWZ\nxiO2najqDKYcwCrgOmCtiJzcpa45QziWC+dYQjie1DieTaz1kQgRGQTOweyIq4EvAJeqqkZrNk5j\nlvu6povdrI2E7bxQVe8ClgI/Bz4PDAG/IyKHA19Q1UYVyQnHcuEcSwjHEwfHc0EoahH5DcxBXg1c\nCjwF+AytFc6noluNNZgB0UikbOcnRWQTsBfYqKoPAQcDLwVOU9V7m+RvhmO5cI4lhOPp6ng26qBn\n4DHAx1X1Var6SVU9A7gbs8L5pujKfRwwpqq/EJEnicizmjboSd/OlwOPA6ZF5AbgScC7gTtEZHPD\n/M1wLBfOsYRwPJ0cz6btjHmIbp2eAVwdvV4effRBzA4ajV4fC+wTkX8GzgVmmjToc7bzBOBBYDfw\nNlX9DeBfgV/R2v6eRziWC+dYQjieODyejSZqEelT1UngZuCZ0dv7AVT1iuj1U6PnQ4HnAT9R1V2q\n+uWOdrYGCmznEuCRqvrHqvof0ee/UtW/UdWfdL7H5bEQj2XSxIbFcCxhYR7PJHTqeDaGqEXkVBE5\nKPq7H+akuPwXsE1EdkW3UvaKdh1we/T3hcAmVX1v1EZPbruI/IGInBWfBFBgO38EPBD9f5+IiKpO\nR697blabiDxcRJbFXstCPJaAwNz+LbRjCeHcjP70ejx7coe0Q0Sejknn+WswKS9iYLNWrsFEVf9e\nRIZUdZ+IHIO57VgqIktU9fuqeo8dBL10axVtyxoR+RTwRsxSY9Oxz4ps5xIRWaqqs6qtmU/xv7sN\nETlSRL6N8ejeISJPAdPH6PYRGn4sAUTkeBG5HvjHhM8WxLG0COdmZ45nI4ga4+f8M3C0iDwrek9U\ndVpERjG3UB8CJoBPiMh/ARcBdwBvAXbYhnppEFhEB2xd9PejVPVSzLagBkW280+A7d3ofxFE6uH3\ngU+o6tOAbwFnisizAVR1ciEcSxHZCLwO+G/gd0XkWFWdjSnNxh9LaClnwrnZmeOpqj33AF6P8XXs\n6zMx+YmnAZfH3j8DuBN4c/R6RbRDng8MRu/1dXt7imwn8ALgHdHfbwXeg5m1NQI8t6nb2baNlwIv\njf5eB3wTk1u6DPgdzEnfuG2M+nZ47G+7vW8Drmj73h9gbvkbv53R6xcu0HMzfjx/r9vnZtd3SNvO\neQ7wVWAWeH/s/d8A/ij6+wLMlNPnA1uAI2Lf62trr7/b21RgOz8YvfcIzNX3bRiF8hLgP6KTYBQ4\nsknb2baNH4reOxP4NrARk2d6XnQsnxMN7iYey2djfMgvA++L3huIfX4LcEbs9c42Emjidr439v5C\nOzfj2/n+6L3jun1udn3HRBszAvwN8F3gJOCJwL8Dy6LPXwWchZmGeTWwD/it+M4hWq2mlx8Z27ky\n+vw84GfAcPT6cOA7wEH2oPf6dmZs40j0+fuA8zFFap4K/AXw3KYdy6ivJwCXY1LNlgD3WXIChqLn\n5wO3xf7Hvj/Y8O18WPTZqxfCuZmxnUdFn/1rN8/NrnrUMZ9rFvgPVX2MGg/oIMxAHo8+vwE4G/gh\n8EngE8DTbDvaZtL3Ggps5/7o878DDgOOjF4vx2zzHWACNb26nTnbOARMAqjqG4HXqOpjVfXrwAbM\nbC6iz5tyLMGcnHcDV6nqOPB1om1R47n3qUnJul5E/l5E/gQ4Pfp8quHbuSb67Hrg/9H8cxOSt3NV\n9NnbMefmUdHrjp6bXSNqEXkV8N8i8jvAdlX9YWynfR04GXOSg5mCeTawS1XfA7wLM7OnJ9OV4iiz\nnap6E/DHwJ+KyDuADwO39/JAh0LbeBKwNfquqOpDYiqovS767KqudLwk4tspItuBW4F7gc+KyH3A\nMPCPIvJmEdmoreDYDcCbMRfgi7rR9zIouJ3/EB2/EYxve0yTz82M7fyQiPzJ/9/euYTIUUVh+Ptj\nxIDjIEhEQQg+QBA1WfiMIBFUcKU4OyVEkYDoJO4FQVGCiyA+sowbFwrCCAoiGXwujOAgiCNuYqIL\nQVE3SogGSY6Le3u6GIPMTPfcuj33/2Doqu66Pfer7rpdderUqbxtPkVf22ZPhxjPA0eA+0iHVz7B\noQAAA9JJREFUTh92XtvcOdR45BxtN5foY2HPPcvaXUnKHLi+b4d1dHwOmAN29O2wBs8ngfnOaweA\n2Tx9Oyl+eWOe30sqQnRDZ/lqQwGr8NxJqtmxvfN6lXHnMXye75B2EiFdnDNbetsstkctaUt+nCYV\nJtkbER8ArwPbJD2TFz0j6YI8fSq3OS8/LiWL18oaPf/KbQYpXD9ExGsR8a0qTP4f0fH8PP9SRMxE\nxNc1OsL/eh4GrpL0bF70V9JRHxHxBekE0+Bo8K2IeCAiFjW84KGqI6Q1eh4lhXkuz203RSrpWS0j\nfJ5T5PS6iDgREYdKb5vr/o+UCme/DHycE8H/JO0xPpoX2QIcBfZJujQSp0mxn8dgqaYrtX3Bu4zT\ns0tUlFs6Jsd/8uMf+T1VkyOs2PNz4AlJF5MugLhT0kOSniZt2McAIuJkfk9FZfHaMXkeh7q+p8sZ\nk+f3y9+3pHOJX4RZ0kr5nXz1Un5uRtIh0oUP7wFvky6IGHAE+GSwF1Kgn6PSgufYHWsauDqs1HOO\nlP/9BvARKd/2CuD+iDjWfcNWPCtl8j0LxIIuJB0eXQssknMOSSvuHuC2PP8C8GCn3cTEu1rxbMFx\nDZ4znXZTnelqL+aw5+R5ll5hB0gxO+icUMkr8n1g1znaVHvipWXPFhztac9aPEufxHkVuEbSvRER\nki6StIeUi/lVRHy6vEHkNTVhtODZgiPY054VeBa9Z2JE/CLpIOmOw9OkZPIfgVsj3VtscNKl9xUz\nCi14tuAI9sSeVdDHzW1/IsWFtgG7I2IBltJ7qjorPiIteLbgCPa0Z8+oZJ8kbSUV2H4zIv5Tq3ej\n0IJnC45gz43GpHoWHagh5TRGzhce/IIV7UAhWvBswRHsudGYRM/iAzXUGQNaD1rwbMER7LnRmDTP\nXgZqY4wxK6fKGgvGGGOGeKA2xpjK6SM9z5iiSLqEVLsB4DLgDPBbnj8VETt76ZgxK8QxatMUSqUs\nT0bEwb77YsxKcejDNI2kQRnSXZI+k/SupBOSXpT0sKQvJS1Kujovt1XSnKSF/HdHvwamBTxQGzNk\nO/A46U7hu0k3cL2FVFh+X17mFdJduG8mlcQ83EdHTVs4Rm3MkIWI+BlA0nFgPj+/CNyVp+8GruuU\n1Z6WNBX5BgHGrAceqI0ZcrozfbYzf5bhtrKJVL/475IdM23j0Icxq2OeYRgESTt67ItpBA/UxqyO\n/cBNkr6R9B0ppm3MuuL0PGOMqRzvURtjTOV4oDbGmMrxQG2MMZXjgdoYYyrHA7UxxlSOB2pjjKkc\nD9TGGFM5HqiNMaZy/gVYc2s6ch90HwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f14349ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "seasonal_sub_series_data['Quarterly Standard Deviation'].plot(color='b')\n",
    "plt.title('Quarterly Quarterly Standard Deviation of Residuals')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('CO2 concentratition')\n",
    "plt.xticks(rotation=30)\n",
    "plt.savefig('plots/ch1/B07887_01_09.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n54f333/fYplSqcTSpUsxd+7cJi+MiMgV1HmxRm5uLt544w2cOHECANCrVy/M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\nxiPYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1432de550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Multiple box plots to visualize central tendency and dispersion of seasonal sub series\n",
    "plt.figure(figsize=(5.5, 5.5))\n",
    "g = sns.boxplot(data=data, y='Residuals', x='Quarter')\n",
    "g.set_title('Quarterly Boxplots of Residuals')\n",
    "g.set_xlabel('Time')\n",
    "g.set_ylabel('CO2 concentratition')\n",
    "plt.savefig('plots/ch1/B07887_01_10.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "inflation = pd.read_excel('datasets/inflation-consumer-prices-annual.xlsx', parse_dates=['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>India</th>\n",
       "      <th>United States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1960-01-01</td>\n",
       "      <td>1.783265</td>\n",
       "      <td>1.509929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1961-01-01</td>\n",
       "      <td>1.752022</td>\n",
       "      <td>1.075182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>3.576159</td>\n",
       "      <td>1.116071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1963-01-01</td>\n",
       "      <td>2.941176</td>\n",
       "      <td>1.214128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1964-01-01</td>\n",
       "      <td>13.354037</td>\n",
       "      <td>1.308615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year      India  United States\n",
       "0 1960-01-01   1.783265       1.509929\n",
       "1 1961-01-01   1.752022       1.075182\n",
       "2 1962-01-01   3.576159       1.116071\n",
       "3 1963-01-01   2.941176       1.214128\n",
       "4 1964-01-01  13.354037       1.308615"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inflation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ecvz2W/ADufUhLC2VzikHH+y7fNgwYOlSYMWKxJfHkC4C3YA75jgSaGYey8xj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\n7tJFjNDaHAcW2nff2QtmEOtudvYJnJdn09DllBU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HBVqxxwj0unUyVQtaURKOCrRij8lo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1410bffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "plt.plot(range(1960,2017), inflation['India'], linestyle='-', marker='*', color='r')\n",
    "plt.plot(range(1960,2017), inflation['United States'], linestyle='-', marker='.', color='b')\n",
    "plt.legend(['India','United States'], loc=2)\n",
    "plt.title('Inflation in Consumer Price Index')\n",
    "plt.ylabel('Inflation')\n",
    "plt.xlabel('Years')\n",
    "plt.savefig('plots/ch1/B07887_01_11.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
